High-Throughput Screening Using Fourier-Transform Infrared Imaging

Sasmaz, Erdem; Mingle, Kathleen; Lauterbach, Jochen A.

doi:10.15302/j-eng-2015040

Cited by 11 publications

(8 citation statements)

References 67 publications

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“…It would be beneficial for future investigations in this area to aim at bridging the gap between simplified descriptor-based screening and complex real models in a manner that considers dynamics during catalytic events. Although high-throughput or combinatorial methods, which have been applied successfully to relevant fields including materials synthesis, serve as powerful tools to discover novel catalysts and catalytic processes, ,,− at the current time these experiments have not been explored fully. As a consequence, there is a lack of good and refined data to guide ML.…”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

et al. 2019

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The discovery and development of catalysts and catalytic processes are essential components to maintaining an ecological balance in the future. Recent revolutions made in data science could have a great impact on traditional catalysis research in both industry and academia and could accelerate the development of catalysts. Machine learning (ML), a subfield of data science, can play a central role in this paradigm shift away from the use of traditional approaches. In this review, we present a user’s guide for ML that we believe will be helpful for scientists performing research in the field of catalysis and summarize recent progress that has been made in utilizing ML to create homogeneous and heterogeneous catalysts. The focus of the review is on the design, synthesis, and characterization of catalytic materials/compounds as well as their applications to catalyzed processes. The ML technique not only enhances ways to discover catalysts but also serves as a powerful tool to establish a deeper understanding of relationships between the properties of materials/compounds and their catalytic activities, selectivities, and stabilities. This knowledge facilitates the establishment of principles employed to design catalysts and to enhance their efficiencies. Despite such advantages of ML, it is noteworthly that the current ML-assisted development of real catalysts remains in its infancy, mainly because of the complexity of catalysis associated with the fact that catalysis is a time-dependent dynamic event. In this review, we discuss how seamless integration of experiment, theory, and data science can be used to accelerate catalyst development and to guide future studies aimed at applications that will impact society’s need to produce energy, materials, and chemicals. Moreover, the limitations and difficulties of ML in catalysis research originating from the complex nature of catalysis are discussed in order to make the catalysis community aware of challenges that need to be addressed for effective and practical use of ML in the field.

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Section: Conclusion and Future Outlookmentioning

confidence: 99%

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

et al. 2019

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“…Tests of catalyst reactivity were carried out in a 16-channel high throughput reactor with a parallel Fourier transform infrared spectroscopy (FTIR) imaging system, which is described in detail elsewhere (Snively et al, 1999 , 2000 , 2001a , b , 2002 ; Hendershot et al, 2005 ; Sasmaz et al, 2015 ). CO oxidation activity tests for the Co 3 O 4 nanoparticles were completed using 50 mg of catalyst per channel under a 2% CO/8% O 2 /N 2 gas stream at a space velocity of 60,000 mL· −1 ·

.…”

Section: Methodsmentioning

confidence: 99%

Synthesis-Structure-Activity Relationships in Co3O4 Catalyzed CO Oxidation

Mingle

Lauterbach

2018

Front. Chem.

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In this work, a statistical design and analysis platform was used to develop cobalt oxide based oxidation catalysts prepared via one pot metal salt reduction. An emphasis was placed upon understanding the effects of synthesis conditions, such as heating regimen and Co2+ concentration on the metal salt reduction mechanism, the resultant nanomaterial properties (i.e., size, crystal structure, and crystal faceting), and the catalytic activity in CO oxidation. This was accomplished by carrying out XRD, TEM, and FTIR studies on synthesis intermediates and products. Additionally, high-throughput experimentation was employed to study the performance of Co3O4 oxidation catalysts over a wide range of reaction conditions using a 16-channel fixed bed reactor equipped with a parallel infrared imaging system. Specifically, Co3O4 nanomaterials of varying properties were evaluated for their performance as CO oxidation catalysts. Figure-of-merits including light-off temperatures and activation energies were measured and mapped back to the catalyst properties and synthesis conditions. Statistical analysis methods were used to elucidate significant property-activity relationships as well as the design rules relevant in the synthesis of active catalysts. It was found that the degree of grain boundary consolidation and anisotropic growth in fcc and hcp CoO intermediates significantly influenced the catalytic activity. By utilizing the discovered synthesis-structure-activity relationships, CO oxidation light off temperatures were decreased to <90°C.

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“…Catalyst materials may contain numerous elements and additional variables such as support material, particle size, dispersion, and loadingthis results in a combinatorial explosion of possibilities. High-throughput testing typically focuses on the composition of catalyst materials and seeks to test as many catalyst compositions as possible. − This is a highly efficient version of the Edisonian trial-and-error approach through which catalysts have been developed for over a century, ,, and requires specialized setups (e.g., parallel reactors or printed catalysts) and rapid approaches for monitoring activity (e.g., thermal or FTIR product detection). − ,, Needless to say, high-throughput combinatorial studies generate a tremendous amount of data, which presents an informatics challenge. For this reason, high-throughput studies were early adopters of machine learning and data science techniques, and several informatics frameworks have been proposed to manage high-throughput catalytic data. ,,,− Reviews of high-throughput catalyst testing have been published previously, − so here we focus primarily on applications of informatics techniques for searching through the high-dimensional space and machine-learning approaches for identifying patterns in the data.…”

Section: From Data To Knowledgementioning

confidence: 99%

“…119−122 This is a highly efficient version of the Edisonian trial-and-error approach through which catalysts have been developed for over a century, 49,50,52 and requires specialized setups (e.g., parallel reactors or printed catalysts) and rapid approaches for monitoring activity (e.g., thermal or FTIR product detection). [119][120][121]123,124 Needless to say, high-throughput combinatorial studies generate a tremendous amount of data, which presents an informatics challenge. For this reason, highthroughput studies were early adopters of machine learning and data science techniques, and several informatics frameworks have been proposed to manage high-throughput catalytic data.…”

Section: From Data To Knowledgementioning

confidence: 99%

Extracting Knowledge from Data through Catalysis Informatics

et al. 2018

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Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and materials informatics but with distinctive challenges arising from the dynamic, surface-sensitive, and multiscale nature of heterogeneous catalysis. The ideas behind catalysis informatics can be traced back decades, but the field is only recently emerging due to advances in data infrastructure, statistics, machine learning, and computational methods. In this work, we review the field from early works on expert systems and knowledge engines to more recent approaches utilizing machine-learning and uncertainty quantification. The data–information–knowledge hierarchy is introduced and used to classify various developments. The chemical master equation and microkinetic models are proposed as a quantitative representation of catalysis knowledge, which can be used to generate explanative and predictive hypotheses for the understanding and discovery of catalytic materials. We discuss future prospects for the field, including improved quantitative coupling of experiment/theory, advanced microkinetic models, and the development of open-source software tools. Ultimately, integration of existing chemical and physical models with emerging statistical and computational tools presents a promising route toward the automated design, discovery, and optimization of heterogeneous catalytic processes.

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High-Throughput Screening Using Fourier-Transform Infrared Imaging

Cited by 11 publications

References 67 publications

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Synthesis-Structure-Activity Relationships in Co3O4 Catalyzed CO Oxidation

Extracting Knowledge from Data through Catalysis Informatics

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